Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband.

Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g.

Raspberry Pi based Modular System for Multichannel Event-Driven Functional Electrical Stimulation Control.

This paper describes the implementation and testing of a modular software for multichannel control of Functional Electrical Stimulation (FES). Moving towards an embedded scenario, the core of the system is a Raspberry Pi, whose different models (with different computing powers) best suit two different system use-cases: user-supervised and stand-alone. Given the need for real-time and reliable FES applications, software processing timings were analyzed for multiple configurations, along with hardware resources utilization.

Combining Action Observation Treatment with a Brain-Computer Interface System: Perspectives on Neurorehabilitation.

Action observation treatment (AOT) exploits a neurophysiological mechanism, matching an observed action on the neural substrates where that action is motorically represented. This mechanism is also known as mirror mechanism. In a typical AOT session, one can distinguish an observation phase and an execution phase.

Motion Analysis for Experimental Evaluation of an Event-Driven FES System.

In this work, a system for controlling Functional Electrical Stimulation (FES) has been experimentally evaluated. The peculiarity of the system is to use an event-driven approach to modulate stimulation intensity, instead of the typical feature extraction of surface ElectroMyoGraphic (sEMG) signal. To validate our methodology, the system capability to control FES was tested on a population of 17 subjects, reproducing 6 different movements.